标题： 雷达斑点：用于自动驾驶场景高保真数据合成和3D重建的雷达高斯斑点技术 显示英文标题

标题： RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes

摘要： 高保真三维场景重建在自动驾驶中起着至关重要的作用，因为它能够从现有数据集中生成新的数据。 这使得模拟安全关键场景以及扩充训练数据集成为可能，而无需承担进一步的数据收集成本。 尽管最近辐射场的进展在使用相机和激光雷达进行三维重建和传感器数据合成方面展示了有希望的结果，但其在雷达方面的潜力尚未得到充分探索。 由于雷达在恶劣天气条件（如雨、雾、雪）下的鲁棒性，它对于自动驾驶至关重要，在这些条件下光学传感器往往表现不佳。 虽然最先进的基于雷达的神经表示在三维驾驶场景重建方面显示出希望，但在存在显著雷达噪声（包括接收器饱和和多路径反射）的场景中性能较差。 此外，它局限于合成预处理、排除噪声的雷达图像，未能解决真实的雷达数据合成问题。 为了解决这些限制，本文提出了RadarSplat，它结合了高斯点绘制与新颖的雷达噪声建模，以实现真实的雷达数据合成和增强的三维重建。 与最先进的方法相比，RadarSplat在雷达图像合成方面取得了更好的结果（+3.4 PSNR / 2.6倍SSIM），并且在几何重建方面也有所改进（-40% RMSE / 1.5倍精度），证明了其在生成高保真雷达数据和场景重建方面的有效性。 项目页面可在https://umautobots.github.io/radarsplat获取。 显示英文摘要

摘要： High-Fidelity 3D scene reconstruction plays a crucial role in autonomous driving by enabling novel data generation from existing datasets. This allows simulating safety-critical scenarios and augmenting training datasets without incurring further data collection costs. While recent advances in radiance fields have demonstrated promising results in 3D reconstruction and sensor data synthesis using cameras and LiDAR, their potential for radar remains largely unexplored. Radar is crucial for autonomous driving due to its robustness in adverse weather conditions like rain, fog, and snow, where optical sensors often struggle. Although the state-of-the-art radar-based neural representation shows promise for 3D driving scene reconstruction, it performs poorly in scenarios with significant radar noise, including receiver saturation and multipath reflection. Moreover, it is limited to synthesizing preprocessed, noise-excluded radar images, failing to address realistic radar data synthesis. To address these limitations, this paper proposes RadarSplat, which integrates Gaussian Splatting with novel radar noise modeling to enable realistic radar data synthesis and enhanced 3D reconstruction. Compared to the state-of-the-art, RadarSplat achieves superior radar image synthesis (+3.4 PSNR / 2.6x SSIM) and improved geometric reconstruction (-40% RMSE / 1.5x Accuracy), demonstrating its effectiveness in generating high-fidelity radar data and scene reconstruction. A project page is available at https://umautobots.github.io/radarsplat.